News Accuracy Bonds

Fake news is a type of yellow journalism or propaganda that consists of deliberate misinformation or hoaxes spread via traditional print and broadcast news media or online social media. This false information is mainly distributed by social media, but is periodically circulated through mainstream media. Fake news is written and published with the intent to mislead in order to damage an agency, entity, or person, and/or gain financially or politically, often using sensationalist, dishonest, or outright fabricated headlines to increase readership, online sharing, and Internet click revenue. (more)

One problem with news is that sometimes readers who want truth instead read (or watch) and believe news that is provably false. That is, a news article may contain claims that others are capable of proving wrong to a sufficiently expert and attentive neutral judge, and some readers may be fooled against their wishes into believing such news.

Yes, news can have other problems. For example, there can be readers who don’t care much about truth, and who promote false news and its apparent implications. Or readers who do care about truth may be persuaded by writing whose mistakes are too abstract or subtle to prove wrong now to a judge. I’ve suggested prediction markets as a partial solution to this; such markets could promote accurate consensus estimates on many topics which are subtle today, but which will eventually become sufficiently clear.

In this post, however, I want to describe what seems to me the simple obvious solution to the more basic problem of truth-seekers believing provably-false news: bonds. Those who publish or credential an article could offer bonds payable to anyone who shows their article to be false. The larger the bond, the higher their declared confidence in their article. With standard icons for standard categories of such bonds, readers could easily note the confidence associated with each news article, and choose their reading and skepticism accordingly.

That’s the basic idea; the rest of this post will try to work out the details.

While articles backed by larger bonds should be more accurate on average, the correlation would not be exact. Statistical models built on the dataset of bonded articles, some of which eventually pay bonds, could give useful rough estimates of accuracy. To get more precise estimates of the chance that an article will be shown to be in error, one could create prediction markets on the chance that an individual article will pay a bond, with initial prices set at statistical model estimates.

Of course the same article should have a higher chance of paying a bond when its bond amount is larger. So even better estimates of article accuracy would come from prediction markets on the chance of paying a bond, conditional on a large bond amount being randomly set for that article (for example) a week after it is published. Such conditional estimates could be informative even if only one article in a thousand is chosen for such a very large bond. However, since there are now legal barriers to introducing prediction markets, and none to introducing simple bonds, I return to focusing on simple bonds.

Independent judging organizations would be needed to evaluate claims of error. A limited set of such judging organizations might be certified to qualify an article for any given news bond icon. Someone who claimed that a bonded article was in error would have to submit their evidence, and be paid the bond only after a valid judging organization endorsed their claim.

Bond amounts should be held in escrow or guaranteed in some other way. News firms could limit their risk by buying insurance, or by limiting how many bonds they’d pay on all their articles in a given time period. Say no more than two bonds paid on each day’s news. Another option is to have the bond amount offered be a function of the (posted) number of readers of an article.

As a news article isn’t all true or false, one could distinguish degrees of error. A simple approach could go sentence by sentence. For example, a bond might pay according to some function of the number of sentences (or maybe sentence clauses) in an article shown to be false. Alternatively, sentence level errors might be combined to produce categories of overall article error, with bonds paying different amounts to those who prove each different category. One might excuse editorial sentences that do not intend to make verifiable newsy claims, and distinguish background claims from claims central to the original news of the article. One could also distinguish degrees of error, and pay proportional to that degree. For example, a quote that is completely made up might be rated as completely false, while a quote that is modified in a way that leaves the meaning mostly the same might count as a small fractional error.

To the extent that it is possible to verify partisan slants across large sets of articles, for example in how people or organizations are labeled, publishers might also offer bonds payable to those than can show that a publisher has taken a consistent partisan slant.

A subtle problem is: who pays the cost to judge a claim? On the one hand, judges can’t just offer to evaluate all claims presented to them for free. But on the other hand, we don’t want to let big judging fees stop people from claiming errors when errors exist. To make a reasonable tradeoff, I suggest a system wherein claim submissions include a fee to pay for judging, a fee that is refunded double if that claim is verified.

That is, each bond specifies a maximum amount it will pay to judge that bond, and which judging organizations it will accept. Each judging organization specifies a max cost to judge claims of various types. A bond is void if no acceptable judge’s max is below that bond’s max. Each submission asking to be paid a bond then submits this max judging fee. If the judges don’t spend all of their max judging fee evaluating this case, the remainder is refunded to the submission. It is the amount of the fee that the judges actually spend that will be refunded double if the claim is supported. A public dataset of past bonds and their actual judging fees could help everyone to estimate future fees.

Those are the main subtleties that I’ve considered. While there are ways to set up such a system better or worse, the basic idea seems robust: news publishers who post bonds payable if their news is shown to be wrong thereby credential their news as more accurate. This can allow readers to more easily avoid believing provably-false news.

A system like that I’ve just proposed has long been feasible; why hasn’t it been adopted already? One possible theory is that publishers don’t offer bonds because that would remind readers of typical high error rates:

The largest accuracy study of U.S. papers was published in 2007 and found one of the highest error rates on record — just over 59% of articles contained some type of error, according to sources. Charnley’s first study [70 years ago] found a rate of roughly 50%. (more)

If bonds paid mostly for small errors, then bond amounts per error would have to be very small, and calling reader attention to a bond system would mostly remind them of high error rates, and discourage them from consuming news.

However, it seems to me that it should be possible to aggregate individual article errors into measures of overall article error, and to focus bond payouts on the most mistaken “fake news” type articles. That is, news error bonds should mostly pay out on articles that are wrong overall, or at least quite misleading regarding their core claims. Yes, a bit more judgment might be required to set up a system that can do this. But it seems to me that doing so is well within our capabilities.

A second possible theory to explain the lack of such a system today is the usual idea that innovation is hard and takes time. Maybe no one ever tried this with sufficient effort, persistence, or coordination across news firms. So maybe it will finally take some folks who try this hard, long, and wide enough to make it work. Maybe, and I’m willing to work with innovation attempts based on this second theory.

But we should also keep a third theory in mind: that most news consumers just don’t care much for accuracy. As we discuss in our book The Elephant in the Brain, the main function of news in our lives may be to offer “topics in fashion” that we each can all riff on in our local conversations, to show off our mental backpacks of tools and resources. For that purpose, it doesn’t much matter how accurate is such news. In fact, it might be easier to show off with more fake news in the mix, as we can then show off by commenting on which news is fake. In this case, news bonds would be another example of an innovation designed to give us more of what we say we want, which is not adopted because we at some level know that we have hidden motives and actually want something else.